Part - 1 Hiwebxseriescom Hot

vectorizer = TfidfVectorizer() X = vectorizer.fit_transform([text])

text = "hiwebxseriescom hot"

Another approach is to create a Bag-of-Words (BoW) representation of the text. This involves tokenizing the text, removing stop words, and creating a vector representation of the remaining words. part 1 hiwebxseriescom hot

Assuming you want to create a deep feature for the text "hiwebxseriescom hot", I can suggest a few approaches: vectorizer = TfidfVectorizer() X = vectorizer

last_hidden_state = outputs.last_hidden_state[:, 0, :] The last_hidden_state tensor can be used as a deep feature for the text. removing stop words

from sklearn.feature_extraction.text import TfidfVectorizer

inputs = tokenizer(text, return_tensors='pt') outputs = model(**inputs)